Decomposing Constraint Networks for Calculating c-Representations
DOI:
https://doi.org/10.1609/aaai.v38i9.28946Keywords:
KRR: Nonmonotonic Reasoning, KRR: Reasoning with Beliefs, KRR: Preferences, KRR: Qualitative Reasoning, KRR: Common-Sense ReasoningAbstract
It is well-known from probability theory that network-based methods like Bayesian networks constitute remarkable frameworks for efficient probabilistic reasoning. In this paper, we focus on qualitative default reasoning based on Spohn’s ranking functions for which network-based methods have not yet been studied satisfactorily. With constraint networks, we develop a framework for iterative calculations of c-representations, a family of ranking models of conditional belief bases which show outstanding properties from a commonsense and formal point of view, that are characterized by assigning possible worlds a degree of implausibility via penalizing the falsification of conditionals. Constraint networks unveil the dependencies among these penalty points (and hence among the conditionals) and make it possible to compute the penalty points locally on so-called safe sub-bases. As an application of our framework, we show that skeptical c-inferences can be drawn locally from safe sub-bases without losing validity.Downloads
Published
2024-03-24
How to Cite
Wilhelm, M., & Kern-Isberner, G. (2024). Decomposing Constraint Networks for Calculating c-Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10740-10747. https://doi.org/10.1609/aaai.v38i9.28946
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Section
AAAI Technical Track on Knowledge Representation and Reasoning